Stochastic mixed model sequencing with multiple stations using reinforcement learning and probability quantiles

Abstract

In this study, we propose a reinforcement learning (RL) approach for minimizing the number of work overload situations in the mixed model sequencing (MMS) problem with stochastic processing times. The learning environment simulates stochastic processing times and penalizes work overloads with negative rewards. To account for the stochastic component of the problem, we implement a state representation that specifies whether work overloads will occur if the processing times are equal to their respective 25%, 50%, and 75% probability quantiles. Thereby, the RL agent is guided toward minimizing the number of overload situations while being provided with statistical information about how fluctuations in processing times affect the solution quality. To the best of our knowledge, this study is the first to consider the stochastic problem variation with a minimization of overload situations.

Dynamic Storage Location Assignment in Warehouses Using Deep Reinforcement Learning

“…Applied research often considers an additional dimension in the problem formulation inspired by real-world use-cases, such as stochasticity [13,14], machine flexibility [15][16][17], dynamic job releases [18], machine failures [19] or multi-objective optimization criteria [20,21]. These studies show the general feasibility of DRL to learn, but are typically not very competitive with expert systems.…”